LAPSE:2024.1067
Published Article
LAPSE:2024.1067
Integration of Carbon Dioxide Removal (CDR) Technology and Artificial Intelligence (AI) in Energy System Optimization
Guanglei Li, Tengqi Luo, Ran Liu, Chenchen Song, Congyu Zhao, Shouyuan Wu, Zhengguang Liu
June 10, 2024
Abstract
In response to the urgent need to address climate change and reduce carbon emissions, there has been a growing interest in innovative approaches that integrate AI and CDR technology. This article provides a comprehensive review of the current state of research in this field and aims to highlight its potential implications with a clear focus on the integration of AI and CDR. Specifically, this paper outlines four main approaches for integrating AI and CDR: accurate carbon emissions assessment, optimized energy system configuration, real-time monitoring and scheduling of CDR facilities, and mutual benefits with mechanisms. By leveraging AI, researchers can demonstrate the positive impact of AI and CDR integration on the environment, economy, and energy efficiency. This paper also offers insights into future research directions and areas of focus to improve efficiency, reduce environmental impact, and enhance economic viability in the integration of AI and CDR technology. It suggests improving modeling and optimization techniques, enhancing data collection and integration capabilities, enabling robust decision-making and risk assessment, fostering interdisciplinary collaboration for appropriate policy and governance frameworks, and identifying promising opportunities for energy system optimization. Additionally, this paper explores further advancements in this field and discusses how they can pave the way for practical applications of AI and CDR technology in real-world scenarios.
Keywords
3E analysis, AI-CDR, climate change, low carbon, sustainable development
Suggested Citation
Li G, Luo T, Liu R, Song C, Zhao C, Wu S, Liu Z. Integration of Carbon Dioxide Removal (CDR) Technology and Artificial Intelligence (AI) in Energy System Optimization. (2024). LAPSE:2024.1067
Author Affiliations
Li G: Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Ji’nan 250061, China
Luo T: College of Water Conservancy and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Liu R: Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
Song C: Higher Information Industry Technology Research Institute, Beijing Information Science and Technology University, Beijing 100192, China
Zhao C: School of International Trade and Economics, University of International Business and Economics, Beijing 100029, China
Wu S: Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Ji’nan 250061, China
Liu Z: Department of Chemical Engineering, The University of Manchester, Manchester M13 9PL, UK [ORCID]
Journal Name
Processes
Volume
12
Issue
2
First Page
402
Year
2024
Publication Date
2024-02-17
ISSN
2227-9717
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Original Submission
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PII: pr12020402, Publication Type: Review
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LAPSE:2024.1067
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https://doi.org/10.3390/pr12020402
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